Overview

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
BMI is highly overall correlated with SkinThicknessHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with BMIHigh correlation
Pregnancies has 111 (14.5%) zerosZeros

Reproduction

Analysis started2026-01-21 16:44:26.115994
Analysis finished2026-01-21 16:44:43.243865
Duration17.13 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8450521
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:43.371527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3695781
Coefficient of variation (CV)0.87634133
Kurtosis0.15921978
Mean3.8450521
Median Absolute Deviation (MAD)2
Skewness0.90167398
Sum2953
Variance11.354056
MonotonicityNot monotonic
2026-01-21T16:44:43.636222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1135
17.6%
0111
14.5%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
4.9%
928
 
3.6%
Other values (7)58
7.6%
ValueCountFrequency (%)
0111
14.5%
1135
17.6%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
4.9%
928
 
3.6%
ValueCountFrequency (%)
171
 
0.1%
151
 
0.1%
142
 
0.3%
1310
 
1.3%
129
 
1.2%
1111
 
1.4%
1024
3.1%
928
3.6%
838
4.9%
745
5.9%

Glucose
Real number (ℝ)

Distinct135
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.65625
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:44.046597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199.75
median117
Q3140.25
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation30.438286
Coefficient of variation (CV)0.25019911
Kurtosis-0.25784742
Mean121.65625
Median Absolute Deviation (MAD)20
Skewness0.5355873
Sum93432
Variance926.48924
MonotonicityNot monotonic
2026-01-21T16:44:44.899693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9917
 
2.2%
10017
 
2.2%
11716
 
2.1%
12514
 
1.8%
12914
 
1.8%
10614
 
1.8%
11114
 
1.8%
10513
 
1.7%
9513
 
1.7%
11213
 
1.7%
Other values (125)623
81.1%
ValueCountFrequency (%)
441
 
0.1%
561
 
0.1%
572
0.3%
611
 
0.1%
621
 
0.1%
651
 
0.1%
671
 
0.1%
683
0.4%
714
0.5%
721
 
0.1%
ValueCountFrequency (%)
1991
 
0.1%
1981
 
0.1%
1974
0.5%
1963
0.4%
1952
0.3%
1943
0.4%
1932
0.3%
1911
 
0.1%
1901
 
0.1%
1894
0.5%

BloodPressure
Real number (ℝ)

Distinct46
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.386719
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:45.445350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.096642
Coefficient of variation (CV)0.16711134
Kurtosis1.098239
Mean72.386719
Median Absolute Deviation (MAD)8
Skewness0.14188502
Sum55593
Variance146.32874
MonotonicityNot monotonic
2026-01-21T16:44:46.137084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7279
 
10.3%
7057
 
7.4%
7452
 
6.8%
7845
 
5.9%
6845
 
5.9%
6443
 
5.6%
8040
 
5.2%
7639
 
5.1%
6037
 
4.8%
6234
 
4.4%
Other values (36)297
38.7%
ValueCountFrequency (%)
241
 
0.1%
302
 
0.3%
381
 
0.1%
401
 
0.1%
444
 
0.5%
462
 
0.3%
485
 
0.7%
5013
1.7%
5211
1.4%
5411
1.4%
ValueCountFrequency (%)
1221
 
0.1%
1141
 
0.1%
1103
0.4%
1082
0.3%
1063
0.4%
1042
0.3%
1021
 
0.1%
1003
0.4%
983
0.4%
964
0.5%

SkinThickness
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.108073
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:46.989200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.35
Q125
median29
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.791221
Coefficient of variation (CV)0.30202003
Kurtosis5.4309871
Mean29.108073
Median Absolute Deviation (MAD)4
Skewness0.83760833
Sum22355
Variance77.285567
MonotonicityNot monotonic
2026-01-21T16:44:47.713315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29244
31.8%
3231
 
4.0%
3027
 
3.5%
2723
 
3.0%
2322
 
2.9%
3320
 
2.6%
1820
 
2.6%
2820
 
2.6%
3119
 
2.5%
1918
 
2.3%
Other values (40)324
42.2%
ValueCountFrequency (%)
72
 
0.3%
82
 
0.3%
105
 
0.7%
116
0.8%
127
0.9%
1311
1.4%
146
0.8%
1514
1.8%
166
0.8%
1714
1.8%
ValueCountFrequency (%)
991
 
0.1%
631
 
0.1%
601
 
0.1%
561
 
0.1%
542
0.3%
522
0.3%
511
 
0.1%
503
0.4%
493
0.4%
484
0.5%

Insulin
Real number (ℝ)

Distinct185
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.67188
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:48.308648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q1121.5
median125
Q3127.25
95-th percentile293
Maximum846
Range832
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation86.38306
Coefficient of variation (CV)0.61407484
Kurtosis16.232455
Mean140.67188
Median Absolute Deviation (MAD)3
Skewness3.3800191
Sum108036
Variance7462.033
MonotonicityNot monotonic
2026-01-21T16:44:49.456824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125378
49.2%
10511
 
1.4%
1409
 
1.2%
1309
 
1.2%
1208
 
1.0%
1807
 
0.9%
947
 
0.9%
1007
 
0.9%
1156
 
0.8%
1356
 
0.8%
Other values (175)320
41.7%
ValueCountFrequency (%)
141
 
0.1%
151
 
0.1%
161
 
0.1%
182
0.3%
221
 
0.1%
232
0.3%
251
 
0.1%
291
 
0.1%
321
 
0.1%
363
0.4%
ValueCountFrequency (%)
8461
0.1%
7441
0.1%
6801
0.1%
6001
0.1%
5791
0.1%
5451
0.1%
5431
0.1%
5401
0.1%
5101
0.1%
4952
0.3%

BMI
Real number (ℝ)

High correlation 

Distinct247
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.455208
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:49.763730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.3
Q336.6
95-th percentile44.395
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.8751768
Coefficient of variation (CV)0.21183586
Kurtosis0.92022171
Mean32.455208
Median Absolute Deviation (MAD)4.6
Skewness0.59923252
Sum24925.6
Variance47.268056
MonotonicityNot monotonic
2026-01-21T16:44:50.267613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.314
 
1.8%
3213
 
1.7%
31.612
 
1.6%
31.212
 
1.6%
33.310
 
1.3%
32.410
 
1.3%
30.19
 
1.2%
32.99
 
1.2%
32.89
 
1.2%
30.89
 
1.2%
Other values (237)661
86.1%
ValueCountFrequency (%)
18.23
0.4%
18.41
 
0.1%
19.11
 
0.1%
19.31
 
0.1%
19.41
 
0.1%
19.52
0.3%
19.63
0.4%
19.91
 
0.1%
201
 
0.1%
20.11
 
0.1%
ValueCountFrequency (%)
67.11
0.1%
59.41
0.1%
57.31
0.1%
551
0.1%
53.21
0.1%
52.91
0.1%
52.32
0.3%
501
0.1%
49.71
0.1%
49.61
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:50.596182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2026-01-21T16:44:50.926919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2586
 
0.8%
0.2546
 
0.8%
0.2075
 
0.7%
0.2615
 
0.7%
0.2595
 
0.7%
0.2385
 
0.7%
0.2685
 
0.7%
0.274
 
0.5%
0.2634
 
0.5%
0.3044
 
0.5%
Other values (507)719
93.6%
ValueCountFrequency (%)
0.0781
0.1%
0.0841
0.1%
0.0852
0.3%
0.0882
0.3%
0.0891
0.1%
0.0921
0.1%
0.0961
0.1%
0.11
0.1%
0.1011
0.1%
0.1021
0.1%
ValueCountFrequency (%)
2.421
0.1%
2.3291
0.1%
2.2881
0.1%
2.1371
0.1%
1.8931
0.1%
1.7811
0.1%
1.7311
0.1%
1.6991
0.1%
1.6981
0.1%
1.61
0.1%

Age
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2026-01-21T16:44:51.214791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2026-01-21T16:44:51.497456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2272
 
9.4%
2163
 
8.2%
2548
 
6.2%
2446
 
6.0%
2338
 
4.9%
2835
 
4.6%
2633
 
4.3%
2732
 
4.2%
2929
 
3.8%
3124
 
3.1%
Other values (42)348
45.3%
ValueCountFrequency (%)
2163
8.2%
2272
9.4%
2338
4.9%
2446
6.0%
2548
6.2%
2633
4.3%
2732
4.2%
2835
4.6%
2929
3.8%
3021
 
2.7%
ValueCountFrequency (%)
811
 
0.1%
721
 
0.1%
701
 
0.1%
692
0.3%
681
 
0.1%
673
0.4%
664
0.5%
653
0.4%
641
 
0.1%
634
0.5%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.6 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Length

2026-01-21T16:44:51.776272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-21T16:44:51.921057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring characters

ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0500
65.1%
1268
34.9%

Interactions

2026-01-21T16:44:40.988759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:26.401529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.956781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:29.249239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:31.207023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:34.982168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:36.895394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:38.952422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:41.373246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:26.850576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:28.117821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:29.506522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:31.560571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:35.246321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:37.190142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:39.193007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:41.621639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.075027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:28.239862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:29.749450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:32.012571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:35.451058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:37.598720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:39.432142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:41.853962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.224670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:28.358943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:30.086447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:32.336333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:35.661089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:37.829517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:39.669982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:42.047561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.326688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:28.477606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:30.280797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:32.653908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:35.912659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:38.038669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:39.924874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:42.251162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.442213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:28.593925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:30.508503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:34.022202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:36.167099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:38.222973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:40.144007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:42.424134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.562957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:28.719335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:30.733796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:34.237714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:36.470954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:38.382379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:40.379852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:42.626021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:27.751544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:29.055657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:30.965540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:34.579572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:36.701166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:38.745736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-21T16:44:40.696307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-21T16:44:52.129060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1210.3660.0430.2820.1880.3140.6070.182
BMI0.1211.0000.2900.1340.2250.2080.3140.0010.550
BloodPressure0.3660.2901.0000.0100.2430.0980.1480.1900.204
DiabetesPedigreeFunction0.0430.1340.0101.0000.0910.0820.173-0.0430.059
Glucose0.2820.2250.2430.0911.0000.4660.4790.1290.188
Insulin0.1880.2080.0980.0820.4661.0000.2520.0960.212
Outcome0.3140.3140.1480.1730.4790.2521.0000.2350.213
Pregnancies0.6070.0010.190-0.0430.1290.0960.2351.0000.091
SkinThickness0.1820.5500.2040.0590.1880.2120.2130.0911.000

Missing values

2026-01-21T16:44:42.921138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-21T16:44:43.106051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.072.035.0125.033.60.627501
1185.066.029.0125.026.60.351310
28183.064.029.0125.023.30.672321
3189.066.023.094.028.10.167210
40137.040.035.0168.043.12.288331
55116.074.029.0125.025.60.201300
6378.050.032.088.031.00.248261
710115.072.029.0125.035.30.134290
82197.070.045.0543.030.50.158531
98125.096.029.0125.032.30.232541
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106.076.029.0125.037.50.197260
7596190.092.029.0125.035.50.278661
760288.058.026.016.028.40.766220
7619170.074.031.0125.044.00.403431
762989.062.029.0125.022.50.142330
76310101.076.048.0180.032.90.171630
7642122.070.027.0125.036.80.340270
7655121.072.023.0112.026.20.245300
7661126.060.029.0125.030.10.349471
767193.070.031.0125.030.40.315230